On the prediction of re-tweeting activities in social networks – a report on WISE 2012 Challenge

Unankard, Sayan, Chen, Ling, Li, Peng, Wang, Sen, Huang, Zi, Sharaf, Mohamed A. and Li, Xue (2012). On the prediction of re-tweeting activities in social networks – a report on WISE 2012 Challenge. In: X. Sean Wang, Isabel Cruz, Alex Delis and Guangyan Huang, Proceedings of the 13th International Conference on Web Information Systems Engineering (WISE 2012). 13th International Conference on Web Information Systems Engineering (WISE 2012), Paphos, Cyprus, (744-754). 28 - 30 November 2012. doi:10.1007/978-3-642-35063-4_61

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Author Unankard, Sayan
Chen, Ling
Li, Peng
Wang, Sen
Huang, Zi
Sharaf, Mohamed A.
Li, Xue
Title of paper On the prediction of re-tweeting activities in social networks – a report on WISE 2012 Challenge
Conference name 13th International Conference on Web Information Systems Engineering (WISE 2012)
Conference location Paphos, Cyprus
Conference dates 28 - 30 November 2012
Proceedings title Proceedings of the 13th International Conference on Web Information Systems Engineering (WISE 2012)   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2012
Sub-type Fully published paper
DOI 10.1007/978-3-642-35063-4_61
Open Access Status
ISBN 9783642350627
9783642350634
ISSN 0302-9743
1611-3349
Editor X. Sean Wang
Isabel Cruz
Alex Delis
Guangyan Huang
Volume 7651
Start page 744
End page 754
Total pages 11
Collection year 2013
Language eng
Abstract/Summary This paper reports on our participation in the Data Mining track of the WISE 2012 Challenge. The challenge is to predict the volume of future re-tweets and possible views for 33 given original short messages (tweets). Towards this, we compare and contrast four different methods and highlight our methods of choice for accomplishing this challenge. The first method is a naïve approach that discovers a regression function based on the popularity of messages and network connectivity. The second approach is to build a classifier that learns a classification model based on the user’s preferences in different categories of topics. The third approach focuses on a network simulation that leverages a Monte Carlo method to simulate re-tweeting paths starting from a root message. The fourth approach uses collaborative filtering to build a recommendation model. The results of these four methods are compared in terms of their effectiveness and efficiency. Finally, insights into predicting message spreading in social networks are also given.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Thu, 11 Apr 2013, 18:12:29 EST by Ms Deborah Brian on behalf of School of Information Technol and Elec Engineering